However, designing effective GNNs, while satisfying these physical rules is highly non-trivial. For instance, SchNet [1]} is computationally efficient, but the model only uses atomic distances in its message passing in order to ensure rotation-invariance of its energy prediction. Consequently, SchNet fails to capture the 3D structure explicitly, resulting in sub-optimal generalization performance.
The recent DimeNet and DimeNet++ [2]}, [3]} additionally capture bond angle information in its message passing, but this comes with the cost of expensive message computations involving atom triplets to ensure the rotation-invariance. As a result, DimeNet necessitates tremendous compute to scale to a massive dataset (Figure REF  (left))—1600 GPU days to train DimeNet++-large.
Moreover, even DimeNet is unable to model an important physical feature of torsion angles [4]}, failing to capture the full 3D information in its message passing.
